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Agricultural Drone Zoning and Deployment Strategy with Multiple Flights Considering Takeoff Point Reach Distance Minimization Ivan Kristianto Singgih
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol 2 No 2 (2021): International Journal of Informatics, Information System and Computer Engineering
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v2i2.7208

Abstract

In the agricultural sector, drones are used to spray chemicals for the plants. A lawn mowing movement pattern is one of the widely used methods when deploying the drones because of its simplicity. A route planner determines some pre-set routes before making the drones to fly based on them. Each drone flight is limited by its battery level or level of spray liquids. To efficiently complete the spraying task, multiple drones need to be deployed simultaneously. In this study, we study a multiple drone zoning and deployment strategy that minimizes the cost to set up equipment at the takeoff points, e.g., between flights. We propose a method to set the flight starting points and directions appropriately, given various target areas to cover. This is the first study that discusses the spraying drone zoning and deployment plan while minimizing the number of takeoff points, which plays an important role in reducing the drone set up and deployment costs. The suggested procedure helps drone route planners to generate good routes within a short time. The generated routes could be used by the planner for their chemical spraying activity and could be used as initial input for their design, which can be improved with the planners’ experience. Our study shows that when generating an efficient route, we must consider the number of flight area levels, directions of the drone movements, the number of U-turns of the drones, and the start points of the drone flights
Air Quality Prediction in Smart City's Information System Ivan Kristianto Singgih
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol 1 No 1 (2020): International Journal of Informatics, Information System and Computer Engineering
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (903.463 KB) | DOI: 10.34010/injiiscom.v1i1.4020

Abstract

The introduction of new technology and computational power enables more data usages in a city. Such a city is called a smart city that records more data related to daily life activities and analyzes them to provide better services. Such data acquisition and analysis must be conducted quickly to support real-time information sharing and support other decision-making processes. Among such services, an information system is used to predict the air quality to ensure people's health in the city. The objective of this study is to compare various machine learning techniques (e.g., random forest, decision tree, neural network, naïve Bayes, etc.) when predicting the air quality in a city. For the comparison, we perform the removal of records with empty values, data division into training and testing datasets, and application of the k-fold cross-validation method. Numerical experiments are performed using a given online dataset. The results show that the three best methods are random forest, Gradient Boosting, and k-nearest neighbors with precision, recall, and f1-score values more than 0.63.